QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M Taylor, Justyna P. Zwolak
TL;DR
QDFlow tackles the data bottleneck in quantum dot device calibration by supplying a physics-based simulator that generates large, labeled synthetic datasets. It combines a self-consistent Thomas-Fermi solver for the charge density $n(x)$ with a dynamic capacitance model, producing realistic charge stability diagrams and ray-based data that reflect gate-driven transitions and dot merging. A flexible noise framework and extensive parameter randomization enable highly diverse, ML-ready datasets, while the open-source design invites community contribution and benchmarking. This work bridges theory, experiment, and data-driven calibration, enabling scalable exploration of QD devices and ML approaches for tuning and control.
Abstract
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, representative datasets designed to capture the full spectrum of data quality encountered in practice, with both high- and low-quality data for training, benchmarking, and validation, with labels capturing key features of the device state. Collating such datasets experimentally is challenging due to limited data availability, slow measurement bandwidths, and the labor-intensive nature of labeling. QDFlow is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. QDFlow combines a self-consistent Thomas-Fermi solver, a dynamic capacitance model, and flexible noise modules to simulate charge stability diagrams and ray-based data closely resembling experiments. With an extensive set of parameters that can be varied and customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.